This survey revisits spatial intelligence research through the lens of cognitive maps, arguing that agents must build a stable, updatable, and inferable internal representation to integrate partial observations into a coherent world model for perception, reasoning, and generation.
HyperAI has compiled a collection of high‑quality open‑source reasoning datasets—including Open‑RL, CHIMERA, Nemotron‑Math‑v2, OmniSpatial, FrontierScience, HotpotQA, VCR, and CIRR—covering math, multi‑step STEM problems, spatial reasoning, scientific tasks, wiki QA, and visual commonsense, all available for download or online use.
Vision‑language models turn continuous images into discrete tokens through patch extraction, encoding, and projection, enabling Transformers to reason jointly over vision and text, but this compression introduces limits in spatial reasoning, counting, and resolution sensitivity that users must understand.
M2-Reasoning-7B, an open‑source 7B multimodal model from Ant Group, combines a high‑quality data pipeline with dynamic multi‑task training and a novel reward function to deliver state‑of‑the‑art performance on both general and spatial reasoning benchmarks, surpassing many larger competitors.
